Medical images contain a wealth of information, such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created ePad, an open source tool enabling researchers and clinicians to create semantic annotations on images. ePad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats, enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as ePad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.
ePAD permits researchers to describe the semantic information in images in a manner that fits within the research workflow could help them to collect the necessary structured image data. This information is stored in compliance with standards developed by the National Cancer Institute's Annotation and Image Markup (AIM) standard format for image metadata.